Current Computer - Aided Drug Design - Volume 7, Issue 1, 2011

The first step in the process of drug development is to determine those lead compounds that demonstrate significant biological activity with regard to a target protein. Because this process is often costly and time consuming, there is a need to develop efficient methodologies for the generation of lead compounds for practical drug design. One promising approach for determining a potent lead compound is computational virtual screening. The biological activities of candidate structures found in virtual libraries are estimated by using quantitative structure activity relationship (QSAR) models and/or computational docking simulations. In virtual screening studies, databases of existing drugs or natural products are commonly used as a source of lead candidates. However, these databases are not sufficient for the purpose of finding lead candidates having novel scaffolds. Therefore, a method must be developed to generate novel molecular structures to indicate high activity for efficient lead discovery. In this paper, we review current trends in structure generation methods for drug design and discuss future directions. First, we present an overview of lead discovery and drug design, and then, we review structure generation methods. Here, the structure generation methods are classified on the basis of whether or not they employ QSAR models for generating structures. We conclude that the use of QSAR models for structure generation is an effective method for computational lead discovery. Finally, we discuss the problems regarding the applicability domain of QSAR models and future directions in this field.

In the absence of three-dimensional (3D) structures of potential drug targets, ligand-based drug design is one of the popular approaches for drug discovery and lead optimization. 3D structure-activity relationships (3D QSAR) and pharmacophore modeling are the most important and widely used tools in ligand-based drug design that can provide crucial insights into the nature of the interactions between drug target and ligand molecule and provide predictive models suitable for lead compound optimization. This review article will briefly discuss the features and potential application of recent advances in ligand-based drug design, with emphasis on a detailed description of a novel 3D QSAR method based on the conformationally sample pharmacophore (CSP) approach (denoted CSP-SAR). In addition, data from a published study are used to compare the CSP-SAR approach to the Catalyst method, emphasizing the utility of the CSP approach for ligand-based model development.

Protein misfolding and aggregation into insoluble amyloid deposits are often associated with neurodegenerative disorders. In particular, the polyglutamine (polyQ) diseases are inherited disorders triggered by the expansion of the polyQ tract over its physiological length in the involved protein. The molecular mechanism of aggregation from the native protein into amyloids involves several steps including protein misfolding, aggregation into oligomers, which seems to be the most toxic species, and, finally rearrangements into mature fibrils. In the present contribution, we review studies, integrating computational and experimental approaches, of polyQ proteins, as well as of the details of the complicate aggregation mechanisms in which aberrant form of polyQ proteins are involved. These aspects are of crucial relevance for a complete understanding of the onset of polyQ conformational diseases and can also shed light on putative therapeutic targets and future development of aggregation inhibitors.

The numerous virtual screening (VS) methods that are used today in drug discovery processes differ mainly by the way they model the receptor and/or ligand and by the approach to perform screening. All these methods have in common that they screen databases of chemical compounds containing up to millions of ligands i.e. ZINC database. Larger databases increase the chances of generating hits or leads, but the computational time needed for the calculations increases not only with the size of the database but also with the accuracy of the VS method and the model. Fast docking methods with atomic resolution require a few minutes per ligand, while molecular dynamics-based approaches still require hundreds or thousands of hours per ligand. Therefore, the limitations of VS predictions are directly related to a lack of computational resources, a major bottleneck that prevents the application of detailed, high-accuracy models to VS The current increase in available computer power at low cost due to novel computational architectures would enhance considerably the performance of the different VS methods and the quality and quantity of the conclusions we can get from screening. In this review, we will discuss recent trends in modeling techniques which, in combination with novel hardware platforms, yield order-of-magnitude improvements in the processing speeds of VS methods. We show the state of the art of VS methods as applied with novel computational architectures and the current trends of advanced computing.

Penicillins and cephalosporins are β-lactam antibiotics widely used to treat bacterial infectious diseases. They mainly target the cell wall biosynthesis pathway to inhibit bacterial growth. The targets, known as penicillin-binding proteins, are enzymes involved in the polymerization of glycan chains, cross-linking them during bacterial cell wall formation. However, the dispensation of these antibiotics has been concomitant with increasing incidence of resistance to them. Reportedly, this is due to the evolvement of two resistance mechanisms in the bacterial pathogens. One is the production of β-lactamases that cleave the β-lactam rings of penicillin and cephalosporin antibiotics, rendering them ineffective against the pathogens. Another is the modification of the targets, resulting in their inability to bind β-lactam antibiotics. Nevertheless, β-lactam antibiotics remain clinically relevant due to their high target specificity in bacteria and low toxicity to humans. Thus, to overcome the continuing emergence of resistance in pathogens, more efficacious β- lactams have to be developed and cephalosporins are often preferred over penicillins due to two alkyl sites in the cephalosporin core structure amenable for modification. Transformed β-lactams are expected to have improved antimicrobial spectra and pharmacokinetics. This is illustrated by the development of two cephalosporins, namely ceftobiprole and ceftaroline, which have shown good antimicrobial activities and are currently undergoing clinical trials. This review will discuss computer-aided studies of three enzymes closely related to cephalosporins: (1) its synthesizing enzyme, deacetoxycephalosporin C synthase, (2) its targets, the penicillin-binding proteins, and (3) its degrading enzyme, the β-lactamases, and their implications in the development of new cephalosporins.